墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
400+累计阅读
434平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
本文是LLM系列文章，针对《On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs》的翻译。
本文考察了LLM使用其内部知识图谱（即他们在预训练期间学习的知识图谱）与知识图谱进行推理的能力。提出了两个研究问题，以研究LLM从预训练知识图谱中回忆信息的准确性以及它们从上下文中推断知识图谱关系的能力。为了解决这些问题，我们使用LLM来执行四个不同的知识图谱推理任务。此外，我们确定了在LLM的知识推理过程中可能发生的两种类型的幻觉：内容幻觉和本体幻觉。我们的实验结果表明，LLM可以从自己的记忆中成功地处理简单和复杂的知识图谱推理任务，也可以从输入上下文中进行推理。
从Axure转Figma？这篇编组、缩放、圆角、评论的对比指南帮你无缝切换
本文为资深Axure设计师提供了向Figma无缝迁移的实战指南。文章深度对比了编组、缩放、圆角、评论等核心操作，重点解析了Figma独特的自动布局、组件系统和实时协作功能，旨在帮助用户快速适应新工具，实现设计思维与工作效率的跃迁。
Cluster Computing投稿
在overleaf中新建picins.sty文件，该文件是导入的包在.tex文件中添加以下内容\usepackage{picins}%实现作者简介%下边这块有几个人复制几段\vspace{0\baselineskip} %这一行控制作者间隔，可视情况改数字!!!!!!!!
DESeq2差异分析结果不会看？用这个R脚本5分钟生成发表级火山图和热图
本文详细介绍了如何使用R语言快速可视化DESeq2差异分析结果，5分钟内生成可直接用于论文发表的火山图和热图。通过解析DESeq2数据结构、展示ggplot2和EnhancedVolcano包的绘图技巧，以及pheatmap包的热图定制方法，帮助研究者高效呈现差异表达基因分析结果。
MySQL服务启动失败？手把手教你排查NET HELPMSG 3534错误（附完整修复流程）
本文针对Windows平台上MySQL服务启动失败并提示NET HELPMSG 3534错误的常见问题，提供了系统性的排查与修复指南。文章深入剖析了错误本质，并手把手教你通过查看MySQL错误日志、检查并初始化数据目录、修正Windows服务配置、排查my.ini配置文件陷阱等核心步骤，彻底解决这一棘手问题，帮助开发者和运维人员快速恢复数据库服务。
MIT6.824-lab2D-2022（日志压缩详细讲解）
从paper中去讲解，并实现关于lab2D的代码。
31、Labview如何搭建程序框架？--状态机到数据采集等常用框架
本文废话不多，直接列举几种框架搭建方式，为了尽量向大家提供干货，本文直接从简介和手把手教搭建框架两方面来讲解，本文提到五种经典框架模式，学会这五种就很够了，分别是：状态机框架(State Machine)、生产者-消费者框架(Producer-Consumer)、主-从框架(Master-Slave)、事件驱动框架(Event-Driven)、分层架构(Layered Architecturep)。这个框架是基于状态机结构的。顾名思义“状态机”就是个“切换程序状态的机器”。本质是一个。
07RK3568 I2S主模式 FPGA
RK3568与fpga通过i2s0连接，fpga连接音频芯片ES8388，RK为i2s主模式。调试时发现可正常采集来自fpga的数据。不可发送数据至fpga。由于i2s后连接fpga这里用了Rockchip的dummy-codec，一个虚拟的编解码器。
STM32实现简单的智能电梯控制
上述代码实现了一个简单的智能电梯控制器。电梯的状态包括IDLE（停止）、UP（向上移动）和DOWN（向下移动）三种状态。通过向上和向下按钮的按下，可以控制电梯移动到指定的楼层。在主循环中，不断读取当前楼层的按钮状态，并根据电梯状态决定执行的动作。具体的楼层数量和按钮的GPIO引脚可以根据实际情况进行调整。要实现简单的智能电梯控制，我们可以使用STM32微控制器来实现。
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
复分析 阿尔福斯(Ahlfors) 第三版答案 英文
答案分为十章，有部分习题没有答案，全英文复分析阿尔福斯(Ahlfors) 第三版答案
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
851
扫一扫
